Model save

README.md

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+---
+library_name: transformers
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: hiera-finetuned-busi
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# hiera-finetuned-busi
+
+This model was trained from scratch on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1380
+- Accuracy: 0.9679
+- F1: 0.9678
+- Precision: 0.9678
+- Recall: 0.9679
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine_with_restarts
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 48
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.2276        | 10.0  | 100  | 0.1666          | 0.9615   | 0.9611 | 0.9640    | 0.9615 |
+| 0.1447        | 20.0  | 200  | 0.1826          | 0.9359   | 0.9352 | 0.9426    | 0.9359 |
+| 0.0882        | 30.0  | 300  | 0.1612          | 0.9615   | 0.9612 | 0.9613    | 0.9615 |
+| 0.0606        | 40.0  | 400  | 0.1380          | 0.9679   | 0.9678 | 0.9678    | 0.9679 |
+
+
+### Framework versions
+
+- Transformers 4.53.1
+- Pytorch 2.7.1+cu126
+- Datasets 3.6.0
+- Tokenizers 0.21.2

model.safetensors

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